Search Cyclr Documentation
Use the below search bar to quickly find what you need in Cyclr’s documentation.
Deep Data Ingestion
Deep data ingestion refers to working with large or complex datasets rather than individual records. A common use case is preparing data for vector databases, where information is converted into embeddings for similarity search and retrieval in AI-driven applications.
With Cyclr, data can be ingested at scale, transformed into embeddings, stored in a vector database, and retrieved as part of an automation workflow. Retrieval results can then be passed to AI models or other connected systems for further use.
In this section, you can learn how to ingest data into vector databases, manage embeddings, and build retrieval workflows within Cyclr.